tv Bloomberg Pursuits Bloomberg October 1, 2017 10:30am-11:01am EDT
megan: above all else, ibm executive ginni rometty is an innovator. ginni: we have estimated a market of $2 trillion to make better decisions. megan: she is also out front as an advocate for progress, improving education, creating jobs, and standing on principle in a time of political controversy. ginni: it is not about a sweet. megan she is not towed by : criticism. ginni: people shoot. megan: coming up, a conversation with ginni rometty on this
bloomberg businessweek: debri ief. ♪ megan: i sat down with ginni rometty at a conference at the new cornell tech campus in new york city and asked her about ibm's commitment to developing artificial intelligence, or , as the company calls it cognitive computing. , ginni: we will need help with important decisions, no matter what they are. with the amount of data, it is the complexity of what is out there, and it doesn't matter what profession you are in. therefore it was this idea that , you could help any decision be better. i am always reminded of an interesting statistic that studies have been done -- maybe some in the room from cornell would agree or not -- that, when asked, what percentage of your decisions are right, what percentage would you guess? megan: 100%. [laughter] megan: exact -- ginni: 100%, exactly. studies, on average, one third are great decisions, one third not optimal, and one third are wrong. when you think about that and how much we have estimated a
market at $2 trillion to make does i -- better decisions becae of that. that is why we call it cognitive and getting through to people that we think that this is about man and machine, not man versus machine. this will be an era that will play out for decades in front of us. ibmn: a lot of people know and ai through watson, and many people know watson through jeopardy. can you talk to us about watson and make it more real world for these people in this room to understand what is done? clients, what is being used as critical business decisions now? ginni: we viewed an era where you would not program machines. they would look at data, understand, reason over it and continue to learn. understand, reason, learn. not program. everything you know today is programmable. an entire era, for decades has , been programmable.
this is the beginning of a new era where you do not program. that, to us, is a big difference mightween what you and i experience in what i call consumer ai, general-purpose versus business. what we set out to do is build an ai platform for business. i think it comes alive with better examples. when i say for business, there will be two big differences to consumers. one is that -- as an example, if you are on your phone and ask, give me the best song in 1950. some of you might ask for 2012, i do not know, but 1950. you do not think about who voted on that? why did they pick that song? you do not think about it. you might say, is this the right diagnosis of this type of cancer? you want to know who trained it, the data, what was the evidence behind it? the idea that for business ai would be vertical, , meaning you would train it to
know medicine, train it to know underwriting of insurance, train it to know financial crimes, train it to know oncology, train it to know weather. i will come back to some of these. that would be one big, big difference. when you train for a vertical, it is not as if there are billions of data points. we have been trading on regulatory. the regulatory world, those of in europe.ing, gdpr ok, it is a regulation. you need to train and interpret something in small amounts of data. vertical knowledge and then the second thing we realized, and i think this is -- if i am a company like i own this is the , most important difference. if i asked you, and i think megan knows the answer to this, guess what percentage of the world's data is searchable, what would be your guess? what would anyone here guess? megan: somebody shout it out?
4%? ginni: somebody said 6%? the answer is 20%. the other 80% lives with those of us who have established businesses. my view was that data has a lot of gold in it. i do not say it you get it today, you absolutely don't. and that leads me to the second big difference of ai towards business. if that is a data and it is my ip in my competitive advantage, i don't want to be training -- because remember, it is training. i am training out an algorithm. i want to be sure those algorithms become mine. so in other words, i want a platform that is my ai, if i can say it that way. even if it operates in cloud. those are the two big differences between what i call consumer, or general-purpose, and business. it knows a domain and profession and you can protect your insight, not just your data, your insight. real life i was telling megan , there are so many examples. we are on a path to hit about a
billion people will have the ir decision impacted by watson in some way. so financial services. and it was interesting for me to watch, some of the big banks in europe have adapted more quickly to this. customer call centers. credit mutual. you take 350,000 emails in a day you don't sort them by keyword , or urgency in how to solve them. they are sorted by importance, sentiments of the client, their customer stats. that is what they use watson for. what is a possible answer? we also own the weather channel, not the tv. anything ontal -- your phone, that is ibm you are hitting when you are doing your weather. we have now introduced watson into that with hurricane irma and harvey. we helped a million conversations,
interactive conversations on how , to prepare for the hurricane. then, it was half a trillion interactions with watson to help 140 airlines reroute. so these to me are things that you learn for business that are what iifferent from think about when people traditionally talk about ai. megan: still to come, ginni rometty responds to critics of cognitive computing. is it a force for good? and is it good for business? ginni: watson is exactly where we thought it would be. talks abouthe navigating politics while engaging with policy. ginni: we are the workforce and it is our job to do that. megan: this is bloomberg. ♪
for artificial intelligence, i asked her how she responds to those who say she is making too much of it. ginni: ibm is an $80 billion company. when people say my goodness, why hasn't this thing grown ibm by two, i think that is an unrealistic expectation. and it is an era and you teach these systems. so those of you who work with them, they have to learn to teach. so actually watson is exactly , where we thought it would be. watson is exactly where we thought it would be. a great example, and i think when you are a pioneer, people do shoot. not deadly, but they shoot. and health care, as an example, i remember when we did our first oncology teaching watson. the very first was long, breast,: cancer. ast, colonre cancer. it took the doctors a year. this is another key point about professional ai. doctors don't want a black-and-white answer, nor does any profession.
if you are a professional, my guess is when you interact with ai, you don't want to say, here is an answer. what a doctor wants is ok, give me the possible answers. tell me why you believe it. can i see the research, the evidence, percent confidence? what more would you like to know? that is really what we are doing. the first cancer took almost a year. we are down to less than 30 days now. year, watson this will have been trained on what causes 80% of the world's cancers. so i find that criticism out of line for what it is we are working on together with doctors. so those of you -- i want to focus the story on this. you know, we are really fortunate in this country, this city. if you got cancer, you will go to a cancer center. in america, only 50% of americans will go to cancer centers. the other 85% -- only 15% of americans will go to cancer centers. will go to their doctor.
go to china and india and you have one oncologist for 1600 patients. your chances of world-class care are literally zero. this idea with watson those are , gold standards. it illustrates beautifully one of the principles of ai in the future. you must know who taught it and what data, and you must be transparent. in my trip to india, a woman had cancer a doctor had never seen. without watson, she would have never had an idea of what the treatment was. so i think you have very different kinds of situations that will be in this world. -- if i can just go full circle, not give you a long, long answer this is why i , am so positive about this world will have more tough problems solved with ai and -- then the issues. there will be issues, and they andthere will be issues, and they -- and they are serious. we will come back and talk about
field education, but the unsolvable is possible because of it. megan: when you look at ai, do you feel like we are going to a where it will displace more point jobs than it creates and we are not doing enough to create this skill, coding, programming, stem education to really push forward the jobs in the future both in ibm and , externally? ginni: do you believe ai will be the course of this? megan: i think ai will displace so many more jobs in so many sectors, like legal and we will , see mass migration to the service sector and i am not sure we are ready for that. isni: so what i do believe when it comes to complete job placement, it will be a small percentage. when it comes to changing a job, what you do, it will be 100%. so that therein, if i park in that thought, that says whoa, different skills. everybody will have to have a different skill because this is a silver thread through all of our jobs. this issue of skills is front
and center to this country and many countries in the world right now without ai. we already have a world that is bifurcating between have and have-nots. a lot of that is based on educational skills. this country has 5 million to 6 million jobs open. that is about skills. this is not being caused by that. and so i think one of the most important things -- i know this is cornell here -- one of the most important things we can do is be clear, not only in this country, around the world, fundamentally, we have to revamp education for this era of man and machine. and that means you can't insist that every person to be productive in society needs to be university or phd graduate. you cannot. it is not true, by the way. we have proven that. megan: there is so little innovation in the education center. why is that? why is the apprenticeships six years? ofy take through two years
college credit, and then it gets into the end -- ginni: this is one of the things i have worked with the administration the most on. in the united states in 2015, half of young people did not have an associate degree or college degree. so the problem is here today in that people need to be retrained. i am far more optimistic on this, that public private -- public-private partnerships can solve this dilemma. i witnessed it. it is not just about doing a few schools. it is called pathway to technology. there are a few hundred of them. it becomes viral, driven by governors and states. i remember when president obama came to the first one, he goes, hey, where the lab? where are all the computers? we were like, this is not what we teach these kids. we are teaching them about math problem-solving that is going to transcend any technology they deal with. by the way, we coined it so you have no bad stereotypes. you are not a blue-collar, you are not a white color, you are a collar.er -- new
other areas are helping. very simple formula, curriculum, math, science, give the kids a mentor, and then you give them a chance at a job. we will be up to 50,000 kids and 300,000 companies have all volunteered. anyone would take a kid and do this. anyone. that is the great thing about this country. we don't look to somebody else to solve this problem. so the idea of scaling this type of new collar apprenticeship and credentialing, and we have some great -- the legislations on the table in the u.s. for some of this, i am optimistic we are going to make a dent in it. but to me, that is a fundamental problem. now ai, when i went to davos in january, we published something. it was called guidelines for transparency in the cognitive era. and all of us have a responsibility. i think it is our responsibility
, if you build this stuff to , guide it safely into the world. one is be clear on the purpose, work with man. we aren't out here to destroy man. the second is be transparent. who trained it, who were the experts, where did the data come from? and if the consumer uses it, you tell him he is using it. as well as a company that owns commercial property. the third thing is be committed to skills. purpose, transparency, and skills. i actually think it is incumbent on us. i'm so passionate about it. the root quality of inequality is in skills and education. while this could exacerbate it, we have it within our hands to help make a difference. megan: up next on this "bloomberg businessweek: debrief ," ginni rometty doesn't like to be praised or pigeonholed just because she is a woman running one of the world's largest tech companies. the responsibility comes with the territory. ginni: people need role models. whether i like that or not, you do have to take that on board. ♪
♪ megan: you were part of president trump's business advisory council, which disbanded in the wake of charlottesville. you said at the time that it was no longer fit for the purpose for which it was created. what did you mean by that? ginni: i cannot tell you how important i think engagement is. so even with my own workforces, i described to them, ibm ceos have engaged with world leaders, in the united states, since woodrow wilson. in june i was with prime minister abe in japan. they'll have many of the same similar issues we deal with. there are important policies, not politics that matter. in fact, i have reinforced with the team over and over it is about policy, not politics.
you get how passionate i am about skills and education, about being competitive, and trade for a digital era. and of course diversity and , inclusion. it is our job -- we are blessed to be able to have an influence, and it is our job to do that. so this strategy policy forum was what i was part of and asked to be a part of. it wasn't a council. it was asked to get input. i think we made a very positive impact on this issue about education and things that can be done. the ministrations to continue to do more things aligned with that. we had very good input on other issues. but what he meant by that then was that that was that was the purpose for. people began to believe that by becoming in any of these vehicles it means you can condone in any way charlottesville, no, we did not. there is nothing to condone about all the activities around
charlottesville. that is what i meant by that. but we would continue to engage, because it is incumbent upon us. it transcends any kind of electoral cycle. i have found, as many of you would -- i have 380,000 employees. so it helps to always explain why we believe these things. no clinicaltax, ch, nobution -- te political contributions, no pac s, never. it has always been about policies. megan: one of the things ibm has been engaged on is the transgender bathroom bill. you are communicated out front -- were communicating out front through charlottesville to your employees. how are those decisions made about what you engage on what issues, policy issues to delve into, and how personally involved are you in those decisions and communicating them to your staff? ginni: i am personally involved, because when you are -- our history of diversity goes back. 1943, ibm had its first woman vice president. megan: what year was that?
ginni: 1943. so i had been surrounded by a culture of diversity inclusion for my whole professional life there. so this is a matter of where that intersection is on where you need to be a thriving business intersects with your values. you cannot speak out on everything. by the way, i don't think speaking is the most important part. it is doing. this we spoke out in north is a really neat key we spoke out in north carolina and texas. point. we had large parts of our population, which we have embraced very, very strongly our lgbt population that were afraid about either place. so in texas, we did 150 meetings with house representatives members. , it is about what you do to communicate to people why these things are important. it is not about a tweet. that is not how change happens. it is about getting in there, rolling your sleeves up communicating why it is an , issue, grassroots efforts.
and that is what we have done on the select issues we think drive home what our values are. and so that is one that is just reinforced. we can't have a workforce afraid of coming to work, and therefore that was why, in general on that , one we were focused on it. megan: keeping on the topic of diversity, one thing i am watching in your interviews throughout the years is your talks about your journey as a female leader and being a role model. i think there is a lot of women who have sympathy with not wanting to be known always as the female ceo, but that having become more important to you during your career, can you touch on that? ginni: yeah, and megan is right. i occasionally will speak of this. early in my career, i would say, please, never reference me as being a woman. this is not about me being a woman. i am on my own merit.
for many, many years until at some point i realized, wait a second, people need role models. whether i like that or not, you have to take got on board. what i learned from my mom, i watch my mom -- yes, she struggled. i'm a proponent for programs in the world that are there for a safety net for people. when we had no money and we had to go on food stamps, but i also watched the pain in her face. she could not wait to get off of those, right, and show us. go back to school, get a degree, get a job, that we would be ok. us -- which has transcended to be as a woman leader as well as a leader -- is don't ever let someone else define who you are. only you define who you are. the world would not define her as a woman whose husband left her, unsuccessful, never educated. she wasn't going to let the world do that. we all -- she never said these words, by the way.
it was only by watching that we internalized that. i think it is true for our company and the country. this idea that you define who you are. maybe we have come full circle in that. i think people look. this isn't 1, 2, 3, 4, 5 generations. we are the team here reinventing it for another generation. the part that has never changed about ibm is innovate technology and apply it to business and society. that is our core. technology is change. this time, it is a new platform. we are basing it on data, ai, the cloud. they will form the new platform of the future. i am betting that the incumbent companies are going to come roaring back, because they realize they have advantages. it is in the data, the know-how.
they are going to marry it with data out in the world, and that data will be the base of the competitive advantage. the new ibm is not only the technology platform. it helps you do that. that is us defining who ibm is and sothat is us defining who ibm is -- and so that is us defining who ibm is of the next generation. that to me, in that way, it is funny to me that it comes full circle. megan: you are not going anywhere. what do you want your legacy to be at ibm? ginni: i am the steward of this great company for my period of time. the team and myself that are there today are to leave ibm just there reinvented for the next era it will be on in another 100 years. it will impact society, there will be jobs, a better future, and it will all be, i think about reshaping ibm for the next , era. in our luckiest moments, when we started -- health care? we will make a difference. forn: thank you so much
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